Jingshu Wang Group

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Aggregating dependent signals with heavy-tailed combination tests

Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

Data denoising with transfer learning in single-cell transcriptomics

Our main research interest is in developing statistical methods and machine learning tools for cutting-edge bio-technologies and genetic problems. We currently work on various problems related to single-cell multi-omics and Mendelian Randomization. We also develop new statistical methods and theory for multiple p-values combination to increasing replicability and/or power, causal inference and factor models, with their applications in statistical genetics.